Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 2
333-336,341

Sentence classification model based on convolution neural network and Bayesian classifier

Li Wenkuan1,2
Liu Peiyu1,2
Zhu Zhenfang3
Liu Wenfeng1,2,4
1. School of Information Science & Engineering, Shandong Normal University, Jinan 250014, China
2. Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Jinan 250014, China
3. School of Information Science & Electric Engineering, Shandong Jiaotong University, Jinan 250014, China
4. School of Computer Science, Heze University, Heze Shandong 274015, China
DOI: 10.19734/j.issn.1001-3695.2018.07.0525

Abstract

The traditional sentence classification model has many disadvantages such as complex feature extraction process and low classification accuracy. This paper used the advantages of the popular deep learning model based convolutional neural network in feature extraction, combined with the traditional sentence classification method, proposed a sentence classification model based on convolutional neural network and Bayesian classifier. The model first used convolutional neural network to extract text features, and secondly used principal component analysis method to reduce the dimensionality of text features. Finally, Bayesian classifier were used to classify sentences. The experimental results show that on Cornell University′s public film review dataset and Stanford Sentiment Treebank dataset, the proposed model is superior to the model using only deep learning or the traditional sentence classification model.

Foundation Support

国家自然科学基金资助项目(61373148)
国家青年自然科学基金资助项目(61502151)
山东省社科规划项目(17CHLJ18,17CHLJ33,17CHLJ30)
山东省自然科学基金资助项目(ZR2014FL010)
山东省教育厅基金资助项目(J15LN34)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.07.0525
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 2
Section: Algorithm Research & Explore
Pages: 333-336,341
Serial Number: 1001-3695(2020)02-003-0333-04

Publish History

[2020-02-05] Printed Article

Cite This Article

李文宽, 刘培玉, 朱振方, 等. 基于卷积神经网络和贝叶斯分类器的句子分类模型 [J]. 计算机应用研究, 2020, 37 (2): 333-336,341. (Li Wenkuan, Liu Peiyu, Zhu Zhenfang, et al. Sentence classification model based on convolution neural network and Bayesian classifier [J]. Application Research of Computers, 2020, 37 (2): 333-336,341. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)